Statistical Join Methods for Data Management

ABSTRACT

Aspects of the disclosure relate to joining data tables. A computing platform may input two or more tables into a statistical join function, which may initiate execution of the statistical join function, and where executing the statistical join function comprises applying one or more of: an end condition function, a partition tables function, or an outer join function to generate a new table that includes information from the two or more tables. The computing platform may send, to a user device, the new table and one or more commands directing the user device to display the new table, which may cause the user device to display the new table.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to co-pendingU.S. application Ser. No. 17/341,909, filed Jun. 8, 2021, and entitled“Statistical Join Methods For Data Management” which is incorporatedherein by reference in its entirety.

BACKGROUND

Aspects of the disclosure relate to computing systems using join methodsfor table combination. In some cases, data tables may be joined byproviding a list of columns and matching the corresponding values (e.g.,using join, outer join, or other methods). Although these methods may besufficient when a number of columns to join is known and fixed, they maybe error prone and/or result in a confusing mechanical expansion of datawhen data is missing and/or different data is used. Such deficienciesmay be amplified as tables increase in size. Accordingly, organizationsmay be unable to effectively combine such large data tablesautomatically, and may be forced to identify other methods forcombination.

SUMMARY

Aspects of the disclosure provide effective, efficient, scalable, andconvenient technical solutions that address and overcome the technicalproblems associated with merging data tables. In accordance with one ormore embodiments of the disclosure, a computing platform comprising atleast one processor, a communication interface, and memory storingcomputer-readable instructions may input two or more tables into astatistical join function, wherein inputting the two or more tables intothe statistical join function initiates execution of the statisticaljoin function, and wherein executing the statistical join functioncomprises: a) identifying whether or not any of the two or more tablescomprise a single row; b) if any of the two or more tables comprise asingle row, executing an end condition function and returning to stepa); c) if none of the two or more tables comprise a single row,executing a partition tables function; d) identifying whether the two ormore tables are both empty; e) if the two or more tables are both empty,returning to step a); f) if the two or more tables are not both empty,executing a merge tables function; g)identifying whether at least one ofthe two or more tables are empty; h) if at least one of the two or moretables are empty, executing an apply outer join function and returningto step a); i) if none of the two or more tables are empty, identifyingwhether a vector R is empty, wherein the vector R is indicative of amerged version of the two or more tables; j) if R is empty, executingthe apply outer join function and returning to step a); and k) if R isnot empty, generating a new table that includes information from the twoor more tables in a single table. The computing platform may send, to auser device, the new table and one or more commands directing the userdevice to display the new table, which may cause the user device todisplay the new table.

In one or more instances, the computing platform may execute the endcondition function by: 1) extracting frequency data for columns of thetwo or more tables with common values by executing a columns with commonvalues function on a vector FDATA, and 2) extracting a vector of columnswith common values from the vector FDATA, by executing the apply outerjoin function, where the vector of columns comprises the vector R.

In one or more examples, the computing platform may execute the columnswith common values function by: for each column in a list of columnsincluded in the two or more tables: 1) storing a first set of values foreach column from a first table of the two or more tables, 2) storing asecond set of values for each column from a second table of the two ormore tables, 3) storing an intersection of the first set of values andthe second set of values, 4) if the intersection includes at least onevalue, storing a number of values comprising the intersection and aproportion of them to the first set of values and the second set ofvalues respectively in the vector FDATA, and 5) outputting the vectorFDATA.

In one or more instances, the computing platform may execute the applyouter join function by: 1) applying an outer join function to join theat least two tables to create a sub-table; 2) adding the sub-table tothe vector R; 3) setting the at least two tables to be empty; and 4)outputting the at least two tables and the vector R.

In one or more embodiments, the computing platform may execute thepartition tables function by: 1) extracting frequency data for columnsfrom the at least two tables with common values; 2) generating a vectorFDATA using the frequency data; 3) extracting, from the vector FDATA,all columns for which values, common between the at least two tables,are greater than 1; 4) generating a vector CCSPLIT using the extractedcolumns; 5) if CCSPLIT contains at least one column: a) generate vectorsof row values for the CCSPLIT columns; b) identify intersection vectorsof the row value vectors and the extracted columns; c) store theintersection vectors as a vector CVAL; and d) for each element of thevector CVAL that contains a value: i) storing a corresponding columnname; ii) applying a split table function to generate new versions ofthe at least two tables; iii) if the at least two tables have adifferent number of rows, applying a rsplit tables function; iv)applying a statistical join function to generate a new sub-table; v)adding the new sub-table to the vector R; and vi) removing, from each ofthe two or more tables, rows belonging to the new version of thecorresponding table of the two of more tables; and 6) outputting the twoor more tables and the vector R.

In one or more instances, the computing platform may perform the splittable function by: 1) storing, in a table Ntable, all rows for whichcolumns of the two or more tables have values in a vector VALS; and 2)outputting the Ntable.

In one or more instances, the computing platform may apply the rsplittable function by: 1) storing a copy of each of the two or more tablesin the corresponding new versions of the two or more tables; 2)extracting the frequency data for columns with common values in thevector FDATA; 3) inputting the vector FDATA into the columns with commonvalues function; 4) removing, from the vector FDATA, all columns forwhich the values, common between the two or more tables are 1 and theirrespective frequencies are also 1, resulting in a vector CC; 5) if thevector CC contains at least a single value: a) generating a vector BMCCthat includes binary masks of the columns in the vector CC, b) for eachbinary mask in the vector BMCC, extracting a vector CMROWS, comprising acollection of corresponding rows from the two or more tables in whichthe corresponding binary mask is applied, and c) for each element of thevector CMROWS that includes at least a single value: i) computing thenew versions of the two or more tables using the split table function;and ii) if the new versions of the two or more tables include at least asingle value, outputting the new versions of the two or more tables.

In one or more embodiments, the computing platform may execute the mergetables function by: 1) extracting frequency data for columns of the twoor more tables in a vector FDATA; 2) inputting the vector FDATA into thecolumns with common values function; 3) based on execution of thecolumns with common values function on the vector FDATA, removing allcolumns for which values, common between the two or more tables are 1and have a frequency of 1 in each of the two or more tables, resultingin a vector CCMERGE; 4) if the vector CCMERGE includes at least a singlevalue: a) generating, for each column of the vector CCMERGE, generate abinary mask; b) for each binary mask: i) extracting a vector CMROWScomprising a collection of corresponding rows from the two or moretables with the given binary mask applied; ii) for each element of thevector CMROWS that includes at least a single value: A) using a splittable function to define new versions of the two or more tables; B)executing an outer join function to generate a new sub-table; C) addingthe new sub-table to the vector R; and D) removing, from each of the twoor more tables, rows belonging to the corresponding new versions of thetwo or more tables; and 5) outputting the two or more tables and thevector R.

These features, along with many others, are discussed in greater detailbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example and not limitedin the accompanying figures in which like reference numerals indicatesimilar elements and in which:

FIGS. 1A-1B depict an illustrative computing environment forimplementing statistical join methods for combining data tables inaccordance with one or more example embodiments;

FIGS. 2A-2E depict an illustrative event sequence for implementingstatistical join methods for combining data tables in accordance withone or more example embodiments;

FIG. 3 depicts an illustrative statistical join method for combiningdata tables in accordance with one or more example embodiments; and

FIGS. 4-6 depict illustrative graphical user interfaces for implementingstatistical join methods for combining data tables in accordance withone or more example embodiments.

DETAILED DESCRIPTION

In the following description of various illustrative embodiments,reference is made to the accompanying drawings, which form a parthereof, and in which is shown, by way of illustration, variousembodiments in which aspects of the disclosure may be practiced. In someinstances, other embodiments may be utilized, and structural andfunctional modifications may be made, without departing from the scopeof the present disclosure.

It is noted that various connections between elements are discussed inthe following description. It is noted that these connections aregeneral and, unless specified otherwise, may be direct or indirect,wired or wireless, and that the specification is not intended to belimiting in this respect.

As a brief introduction to the concepts described further herein, one ormore aspects of the disclosure relate to an improved method for joiningone or more data tables together. For example, existing table joinalgorithms may work well when numbers of columns to join are known andfixed. However, they might not work well with different or missing data.Under such conditions, such matches may result in a confusing mechanicalexpansion of data. For example, a simple two row table may yield fourrows. When data is larger, results may grow even faster.

Accordingly, described herein is a statistical join function that workson the basis of splitting tables into similar sub-tables, which may bemore likely to be joined together. The methods described hereinimplement statistical measures to decide which operation (e.g.,splitting or merging) to apply.

The algorithm, statistical join or SJoin for short, may aggregate twotables horizontally. It may work on the basis of splitting the tablesinto similar sub-tables, which may be more likely to be joined together.For example, a subset of rows from each table (e.g., tables TA and TB)may be identified to share similar features, and may be extracted toform a new sub-table SubTABi, where the rows from table TA, precedethese from table TB and i is the index of the new sub-table, startingfrom 1. The feature similarity may be based on the probability ofsimilarity in rows and columns. For each corresponding column in TA andTB, the sets of values are extracted, and respectively labelled SCA andSCB. The intersection of the two sets may be determined and stored asthe number of common values and the proportion of the common valuesagainst the length of SCA and SCB respectively.

When the split and match options are not applicable, the normal outerjoin operation may be used to join the tables. This may be the fall backcondition.

The process may continue until there are no more rows in the originaltables. The set of sub-tables are used to form a new table whichrepresents a most likely match of the two original tables.

Aspects described herein may include splitting large tables intosmaller, more manageable tables, and/or the use of various statisticalmeasures to determine or identify which operation (splitting or merging)to apply.

This may be particularly applicable to storage and manipulation of tradedata. For example, a large number of attributes may be extracted pertrade, and may be compared with the data in a similarly organized table.It may be important for the correct rows to be matched and for the rowsto be as few as possible. Traditional methods fail to deliver a stable,extensible solution and the data may bloat.

FIGS. 1A-1B depict an illustrative computing environment that implementsstatistical join methods for combining data tables in accordance withone or more example embodiments. Referring to FIG. 1A, computingenvironment 100 may include one or more computer systems. For example,computing environment 100 may include a data management platform 102, atrading system 103, and a user device 104.

As described further below, data management platform 102 may be acomputer system that includes one or more computing devices (e.g.,servers, server blades, or the like) and/or other computer components(e.g., processors, memories, communication interfaces) that may be usedto host one or more data tables and execute one or morefunctions/algorithms to manipulate the data tables. For example, thedata management platform 102 may be configured to perform a statisticaljoin algorithm to combine one or more tables.

Trading system 103 may be or include one or more computing devices(servers, server blades, or the like) that may be configured to executeor otherwise perform one or more trades (e.g., stock trades, fundtrades, and/or other trades). The trading system 103 may be configuredto communicate with the data management platform 102 to send informationabout the trades.

User device 104 may be a laptop computer, desktop computer, mobiledevice, tablet, smartphone, or the like that may be used by anindividual to view or otherwise access information stored at the datamanagement platform 102 (e.g., one or more data tables). In someinstances, user device 104 may be configured to display one or more userinterfaces (e.g., which may include the one or more data tables).

Computing environment 100 also may include one or more networks, whichmay interconnect data management platform 102, trading system 103,and/or user device 104. For example, computing environment 100 mayinclude a network 101 (which may interconnect, e.g., data managementplatform 102, trading system 103, and/or user device 104).

In one or more arrangements, data management platform 102, tradingsystem 103, and/or user device 104 may be any type of computing devicecapable of sending and/or receiving requests and processing the requestsaccordingly. For example, data management platform 102, trading system103, user device 104, and/or the other systems included in computingenvironment 100 may, in some instances, be and/or include servercomputers, desktop computers, laptop computers, tablet computers, smartphones, or the like that may include one or more processors, memories,communication interfaces, storage devices, and/or other components. Asnoted above, and as illustrated in greater detail below, any and/or allof data management platform 102, trading system 103, and/or user device104 may, in some instances, be special-purpose computing devicesconfigured to perform specific functions.

Referring to FIG. 1B, data management platform 102 may include one ormore processors 111, memory 112, and communication interface 113. A databus may interconnect processor 111, memory 112, and communicationinterface 113. Communication interface 113 may be a network interfaceconfigured to support communication between data management platform 102and one or more networks (e.g., network 101, or the like). Memory 112may include one or more program modules having instructions that whenexecuted by processor 111 cause data management platform 102 to performone or more functions described herein and/or one or more databases thatmay store and/or otherwise maintain information which may be used bysuch program modules and/or processor 111. In some instances, the one ormore program modules and/or databases may be stored by and/or maintainedin different memory units of data management platform 102 and/or bydifferent computing devices that may form and/or otherwise make up datamanagement platform 102. For example, memory 112 may have, host, store,and/or include data management module 112 a and data management database112 b.

Data management module 112 a may have instructions that direct and/orcause data management platform 102 to execute statistical join methodsto combine tables, as discussed in greater detail below. Data managementdatabase 112 b may store information used by data management module 112a and/or data management platform 102 in application of advancedtechniques to join data tables, and/or in performing other functions.

FIGS. 2A-2E depict an illustrative event sequence for performingstatistical join methods for combining data tables in accordance withone or more example embodiments. Referring to FIG. 2A, at step 201, thetrading system 103 may establish a connection with data managementplatform 102. For example, the trading system 103 may establish a firstwireless data connection with the data management platform 102 to linkthe trading system 103 with the data management platform 102 (e.g., inpreparation for sending trade data). In some instances, the tradingsystem 103 may identify whether or not a connection is alreadyestablished with the data management platform 102. If a connection isalready established with the data management platform 102, the tradingsystem 103 might not re-establish the connection. If a connection is notyet established with the data management platform 102, the tradingsystem 103 may establish the first wireless data connection as describedabove.

At step 202, the trading system 103 may send trade data (e.g., accounts,amounts, timestamps, and/or other information) to the data managementplatform 102. For example, the trading system 103 may send trade data tothe data management platform 102 while the first wireless dataconnection is established.

At step 203, the data management platform 102 may receive the trade datasent at step 202. For example, the data management platform 102 mayreceive the trade data via the communication interface 113 and while thefirst wireless data connection is established. Although a single sourceof trading data (e.g., trading system 103) is shown, this is merely forillustrative purposes. For example, in some instances, parameters may beextracted for the same trade from multiple systems. In some instances,in receiving the trade data, the data management platform 102 mayreceive parameters in which values are missing and/or different. In someinstances, the trade data for a single trade may correspond to multiplerows.

At step 204, the data management platform 102 may store the trade datareceived at step 203. For example, the data management platform 102 maystore the trade data in one or more tables, such as table A and table B,which are illustrated in tables 405 and 505 (shown in FIGS. 4 and 5respectively).

At step 205, the user device 104 may receive a request to join two ormore tables stored at the data management platform 102. For example, theuser device 104 may receive the request from a system administrator,data manager, and/or other individual.

Referring to FIG. 2B, at step 206, the user device 104 may establish aconnection with the data management platform 102. For example, the userdevice 104 may establish a second wireless connection with the datamanagement platform 102 to link the user device 104 to the datamanagement platform 102 (e.g., in preparation for sending a request tojoin the tables). In some instances, the user device 104 may identifywhether or not a connection is already established with the datamanagement platform 102. If a connection is already established with thedata management platform 102, the user device 104 might not re-establishthe connection. If a connection is not yet established with the datamanagement platform 102, the user device 104 may establish the secondwireless data connection as described herein.

At step 207, the user device 104 may send a request to join the two ormore tables to the data management platform 102. For example, the userdevice 104 may send the request to join the two or more tables while thesecond wireless data connection is established.

At step 208, the data management platform 102 may receive the request tojoin the two or more tables. For example, the data management platform102 may receive the request to join the two or more tables via thecommunication interface 113 and while the second wireless dataconnection is established. In some instances, rather than receiving arequest from the user device 104 to join the two or more tables, thedata management platform 102 may identify that two or more tables shouldbe joined (e.g., to conserve memory, computing resources, or the like).In these instances, steps 205-208 might not be performed.

At step 209, based on identifying that the two or more tables should bejoined, the data management platform 102 may initiate a statistical joinfunction, which is further described below with regards to steps209-220. For example, two tables may be joined by providing a list ofcolumns, the values of which are to be matched. Existing solutions maybe sufficient when a number of columns to join is known and fixedthroughout the use of the logic. However, such solutions might not workwell with different or missing data. Under such conditions, such matchesmay result in a confusing mechanical expansion of data.

The algorithm, statistical join (or sJoin) may be used by the datamanagement platform 102 to aggregate tables horizontally. It may work bysplitting the tables into similar sub-tables, which may be betterconfigured/formatted to be joined together. In doing so, statisticalmeasures may be used to determine or identify which operation (e.g.,splitting or merging) should be applied.

Briefly, and as described further below, a subset of rows from eachtable (e.g., TA and TB) may be identified to share similar features andextracted to form a new sub-table SubTAB_(i), where the rows from tableTA, precede those from table TB and i is the index of the new sub-table,starting from 1. The feature similarity may be based on the probabilityof similarity in rows and columns. For each corresponding column in TAand TB, the sets of values are extracted, and respectively labelled SCAand SCB. The intersection of the two sets may be determined and storedas the number of common values and proportion of the common valuesagainst the length of SCA and SCB respectively.

When the split and match options are not applicable, the normal outerjoin operation may be used to join the tables as a fall back condition.

The process may continue until there are no more rows in the originaltables. The set of sub-tables are used to form a new table whichrepresents a most likely match of the two original tables.

Although two example tables TA and TB are described herein, this ispurely arbitrary and may be renamed and/or reordered without departingfrom the scope of the disclosure.

In some instances, the selection of columns on which the statisticaljoin algorithm may operate may be user dependent. It might not benecessary to be continuous as in the tables they refer to. However, thecolumns should belong to both tables and have the same type. This mayallow much larger tables to be joined by using a subset of columns tojoin. For example, TA may comprise 10 columns and 10 rows, but table TBmay include 7 columns and 20 rows. Assuming only 5 of the columns willbe used in the joining process, the expected table will have 17 columnsand 20 rows if the match is successful.

Execution of the statistical join algorithm may assume several baselineparameters. For example, it may be assumed that table functionality isavailable (e.g., tables are constructs that may be partitioned bysplitting to tables with fewer rows, up to a single row, and aggregatedinto larger tables by adding/appending tables at the top or bottom ofthem). In addition, tables TA and TB may have identical types of valuesfor the columns of interest (TA and TB are used herein to indicate theorder in which resulting rows may be created (e.g., row from table TAfollowed by its matching row from table TB)). Furthermore, these methodsassume that the outer join operation and the ability to use vectors areavailable, and that there is more than one element in the columns ofinterest list.

Accordingly, upon initiating the statistical join algorithm, the datamanagement platform 102 may use the following as inputs: the tables(e.g., TA and TB), a vector/list of columns to operate on (COLS), and aflag indicating whether columns with the same name are to be merged intoa single column. Additionally, the data management platform 102 may seta vector R as an empty vector that may be used to store new mergedsub-tables.

The data management platform 102 may identify whether both tables (e.g.,TA and TB) have rows. If so, the data management platform 102 mayproceed to step 210. If both tables do not have rows, the datamanagement platform 102 may return to step 204 to await another tablejoin request, or the event sequence may end.

At step 210, the data management platform 102 may identify whethereither of the tables (e.g., TA and TB) have more than one row. If so,the data management platform 102 may proceed to step 211. If not, thedata management platform 102 may proceed to step 212.

Referring to FIG. 2C, at step 211, the data management platform 102 mayexecute an end condition function. For example, the data managementplatform 102 may extract frequency data for columns with common valuesin a vector (FDATA). In some instances, to do so, the data managementplatform 102 may execute a columns with common values function. Forexample, the data management platform 102 may set FDATA to be empty.Then, for each column in COLS, the data management platform 102 maystore a first set of values from the column from TA (e.g., in SCA) and asecond set of values from the column in TB (e.g., in SCB). The datamanagement platform 102 may then store the intersection of these valuesets (e.g., of SCA and SCB) in, for example, SCC. If SCC includes atleast one value, the data management platform 102 may store, in FDATA,the number of entries in SCC and the proportion of them to the entriesof SCA and SCB respectively, and may then return FDATA. Otherwise, ifSCC is empty, the data management platform 102 may simply return FDATA.

As a particular example, using TA and TB (as illustrated in FIGS. 4 and5 ), column 1 of TA includes a single value (“Book_A”). Column 1 of TBincludes two values (“Book_A” and “NA”). Accordingly, column 1 of TAincludes 1 value, all values from column 1 of TA are represented incolumn 1 of TB, and the values in column 1 of TA make up 50% of thevalues in column 1 of TB. As such, the data management platform 102 mayoutput, for value 0, {Value0: [1, 1, 0.5]} in FDATA, where 1 indicatesthe number of values in TA column 1, 1 indicates that all values in TAcolumn 1 are represented in TB column 1, and 0.5 indicates that thevalues from TA column 1 make up half of the available values in TBcolumn 1.

Returning to the execution of the end condition function, the datamanagement platform 102 may execute an apply outer join function. Forexample, the data management platform 102 may execute a standard outerjoin function to join the two tables TA and TB together, resulting in anew sub-table SubTAB_(i). In some instances, the data managementplatform 102 may use the FLAG_MERGE_KEYS flag to identify whether or notcolumns with the same name should be merged into a single column. Thedata management platform 102 may then add the new sub-table SubTAB_(i)to the vector R, set the tables TA and TB to empty, and return/outputTA, TB, and R.

After completing execution of the end condition function to return TA,TB, and R, the data management platform 102 may then return to step 209.

At step 212, the data management platform 102 may execute a partitiontables function. For example, the data management platform 102 mayextract frequency data for columns with common values in FDATA byexecuting a columns with common values function (e.g., the columns withcommon values function as described above at step 211). The datamanagement platform 102 may subsequently extract all columns, fromFDATA, for which the values, common between the tables TA and TB arehigher than 1, and may store these extracted columns in a vectorCCSPLIT. The data management platform 102 may identify whether thevector CCSPLIT includes at least a single value. If so, the datamanagement platform 102 may generate vectors of the row values for theCCSPLIT columns and may find their intersection vectors. The datamanagement platform 102 may store these intersection vectors in vectorCVAL. Then, for each non-empty element in the CVAL vector, the datamanagement platform 102 may store corresponding column names in a vectorSPLITCC and may use a split table function to set TA1 and TA2. Forexample, in applying the split table function, the data managementplatform 102 may store, in each of TA1 and TA2 respectively (e.g.,NTable), all rows for which columns in COLS have values in the CVALvector, and may return these tables TA1 and TA2.

After executing the split table function, the data management platform102 may identify whether TA1 and TB1 have different numbers of rows. Ifso, the data management platform 102 may execute an Rsplit tablefunction.

In executing the Rsplit table function, the data management platform 102may store a copy of TA in TA1 and a copy of TB in TB1. The datamanagement platform 102 may extract frequency data for columns withcommon values in FDATA by executing the columns with common valuesfunction as described above. Then, using FDATA, the data managementplatform 102 may remove all columns for which the values, common betweenthe tables TA and TB are 1, and their respective frequencies are 1 and1, and may set this as a vector CC. If the vector CC is empty, the datamanagement platform 102 may simply return values of TA1 and TB1. If thevector CC includes at least a single value, the data management platform102 may generate binary masks (BMCC) of the columns in vector CC,excluding the one with zeros (e.g., if there are only 2 columns thenthere are three masks 01, 10, and 11). For each binary mask, the datamanagement platform 102 may extract a vector CMROWS as the collection ofcorresponding rows from tables TA and TB with binary masks applied(e.g., columns with corresponding values of 0 are ignored). For eachnon-empty element in the vector CMROWS, the data management platform 102may set TA1 and TB1 using the split table function as described above.Then, if TA1 and TB1 include at least a single value, the datamanagement platform 102 may return values of TA1 and TB1. Otherwise, ifTA1 and TB1 are empty, the data management platform 102 may continue toperform the Rsplit tables function.

Returning to the partition tables function, the data management platform102 may define new sub-table SubTAB_(i), as described above, to thevector R. The data management platform 102 may then remove from TA therows belonging to TA1 and remove from TB the rows belonging to TB1. Thedata management platform 102 may then return/output TA, TB, and thevector R.

At step 213, the data management platform 102 may identify whether TAand TB are both empty. If so, the data management platform 102 return tostep 209. Otherwise, the data management platform 102 may proceed tostep 214.

At step 214, the data management platform 102 may execute a merge tablesfunction to define TA, TB, and the vector R. For example, the datamanagement platform 102 may extract frequency data for columns withcommon values in FDATA (e.g., using the columns with common valuesfunction as described above). From FDATA, the data management platform102 may remove all columns for which the values, common between thetables TA and TB are 1 and their respective frequencies are 1 and 1, andset these removed columns as a vector CCMERGE. If the vector CCMERGE isempty, the data management platform 102 may simply return values of TA,TB, and the vector R. If the vector CCMERGE includes at least a singlevalue, the data management platform 102 may generate binary masks of thecolumns in CCMERGE, excluding the one with zeros (e.g., if there areonly 2 columns then there are 3 masks 01, 10, and 11). For each binarymask, the data management platform 102 may extract a vector CMROWS asthe collection of corresponding rows from tables TA and TB with thebinary masks applied (while ignoring the columns with correspondingvalues of 0). For each non-empty element in CMROWS, the data managementplatform 102 may set TA1 and TA2 using the split table function asdescribed above, and may set SubTAB_(i) using the apply outer joinfunction as described above. The data management platform 102 may thenadd the new sub-table SubTAB_(i) to the sub-tables vector R, remove fromTA the rows that belong to TA1, and remove the rows from TB that belongto TB1. The data management platform 102 may subsequently return/outputTA, TB, and the vector R.

Referring to FIG. 2D, at step 215, the data management platform 102 mayidentify whether TA and TB are both empty. If so, the data managementplatform 102 may return to step 209. If not, the data managementplatform 102 may proceed to step 216.

At step 216, the data management platform 102 may identify whether TA orTB are empty. If not, the data management platform 102 may proceed tostep 218. If so, the data management platform 102 may proceed to step217.

At step 217, the data management platform 102 may execute an apply outerjoin function (e.g., as described above) to define TA, TB, and thevector R, and may return to step 209. At step 218, the data managementplatform 102 may identify whether the vector R is empty. If the vector Ris includes at least one value, the data management platform 102 mayproceed to step 220. If the vector R is empty, the data managementplatform 102 may proceed to step 219.

Referring to FIG. 2E, at step 219, the data management platform 102 mayexecute an apply outer join function as described above, and return tostep 209. At step 220, the data management platform 102 may create a newtable TAB from the vector R of created sub-tables SubTAB_(i). Forexample, the data management platform 102 may create a new table thatresembles table 605, which is illustrated in FIG. 6 , and that includesthe data from TA and TB (e.g., as shown in FIGS. 4-5 ) in a singletable. At this point, the statistical join function may be complete.

At step 221, the data management platform 102 may send the new table tothe user device 104. For example, the data management platform 102 maysend the new table via the communication interface 113 and while thesecond wireless data connection is established. In some instances, thedata management platform 102 may also send one or more commandsdirecting the user device 104 to display the new table.

At step 222, the user device 104 may receive the new table sent at step220. For example, the user device 104 may receive the new table whilethe second wireless data connection is established. In some instances,the user device 104 may also receive one or more commands directing theuser device 104 to display the new table.

At step 223, based on or in response to the one or more commandsdirecting the user device 104 to display the new table, the user device104 may display the new table. For example, the user device 104 maydisplay a graphical user interface that includes the table 605.

Although steps 201-223 as described above primarily refer tomanipulation of data tables including trade data, these steps may beperformed to manipulate tables that include any other data withoutdeparting from the scope of the disclosure.

FIG. 3 depicts an illustrative method for performing statistical joinmethods for combining data tables in accordance with one or more exampleembodiments. Referring to FIG. 3 , at step 305, a computing platformhaving at least one processor, a communication interface, and memory mayset a vector R to empty. At step 310, the computing platform mayidentify whether two tables (to be joined) both have rows. If not, thecomputing platform may return to step 305. If so, the computing platformmay proceed to step 315.

At step 315, the computing platform may identify whether any tables haveat least one row. If not, the computing platform may proceed to step325. If so, the computing platform may proceed to step 320.

At step 320, the computing platform may execute an end conditionfunction and return to step 310. At step 325, the computing platform mayexecute a partition tables function. At step 330, the computing platformmay identify whether all tables are empty. If so, the computing platformmay return to step 310. If not, the computing platform may proceed tostep 335.

At step 335, the computing platform may execute a merge tables function.At step 340, the computing platform may identify whether all tables areempty. If so, the computing platform may return to step 310. If not, thecomputing platform may proceed to step 345.

At step 345, the computing platform may identify whether any tables areempty. If not, the computing platform may proceed to step 355. If so,the computing platform may proceed to step 350. At step 350, thecomputing platform may execute an apply outer join function, and thenreturn to step 310.

At step 355, the computing platform may identify whether vector R isempty. If not, the computing platform may proceed to step 365. If so,the computing platform may proceed to step 360. At step 360, thecomputing platform may execute an apply outer join function, and returnto step 310. At step 365, the computing platform may create a new table.At step 370, the computing platform may output the new table.

One or more aspects of the disclosure may be embodied in computer-usabledata or computer-executable instructions, such as in one or more programmodules, executed by one or more computers or other devices to performthe operations described herein. Generally, program modules includeroutines, programs, objects, components, data structures, and the likethat perform particular tasks or implement particular abstract datatypes when executed by one or more processors in a computer or otherdata processing device. The computer-executable instructions may bestored as computer-readable instructions on a computer-readable mediumsuch as a hard disk, optical disk, removable storage media, solid-statememory, RAM, and the like. The functionality of the program modules maybe combined or distributed as desired in various embodiments. Inaddition, the functionality may be embodied in whole or in part infirmware or hardware equivalents, such as integrated circuits,application-specific integrated circuits (ASICs), field programmablegate arrays (FPGA), and the like. Particular data structures may be usedto more effectively implement one or more aspects of the disclosure, andsuch data structures are contemplated to be within the scope of computerexecutable instructions and computer-usable data described herein.

Various aspects described herein may be embodied as a method, anapparatus, or as one or more computer-readable media storingcomputer-executable instructions. Accordingly, those aspects may takethe form of an entirely hardware embodiment, an entirely softwareembodiment, an entirely firmware embodiment, or an embodiment combiningsoftware, hardware, and firmware aspects in any combination. Inaddition, various signals representing data or events as describedherein may be transferred between a source and a destination in the formof light or electromagnetic waves traveling through signal-conductingmedia such as metal wires, optical fibers, or wireless transmissionmedia (e.g., air or space). In general, the one or morecomputer-readable media may be and/or include one or more non-transitorycomputer-readable media.

As described herein, the various methods and acts may be operativeacross one or more computing servers and one or more networks. Thefunctionality may be distributed in any manner, or may be located in asingle computing device (e.g., a server, a client computer, and thelike). For example, in alternative embodiments, one or more of thecomputing platforms discussed above may be combined into a singlecomputing platform, and the various functions of each computing platformmay be performed by the single computing platform. In such arrangements,any and/or all of the above-discussed communications between computingplatforms may correspond to data being accessed, moved, modified,updated, and/or otherwise used by the single computing platform.Additionally or alternatively, one or more of the computing platformsdiscussed above may be implemented in one or more virtual machines thatare provided by one or more physical computing devices. In sucharrangements, the various functions of each computing platform may beperformed by the one or more virtual machines, and any and/or all of theabove-discussed communications between computing platforms maycorrespond to data being accessed, moved, modified, updated, and/orotherwise used by the one or more virtual machines.

Aspects of the disclosure have been described in terms of illustrativeembodiments thereof. Numerous other embodiments, modifications, andvariations within the scope and spirit of the appended claims will occurto persons of ordinary skill in the art from a review of thisdisclosure. For example, one or more of the steps depicted in theillustrative figures may be performed in other than the recited order,and one or more depicted steps may be optional in accordance withaspects of the disclosure.

What is claimed is:
 1. A computing platform comprising: at least oneprocessor; a communication interface communicatively coupled to the atleast one processor; and memory storing computer-readable instructionsthat, when executed by the at least one processor, cause the computingplatform to: initiate execution of a statistical join function on two ormore tables, wherein executing the statistical join function comprises:a) if any of the two or more tables comprise a single row, executing anend condition function and repeating step b), wherein executing the endcondition function joins at least two of the two or more tablescomprising the single row and causes the at least two of the two or moretables comprising the single row to no longer comprise the single row;b) if none of the two or more tables comprise a single row, executing apartition tables function; c) if the two or more tables are both empty,executing a statistical join function to create a new table of the twoor more tables, and returning to step b); d) if the two or more tablesare not both empty, executing a merge tables function; e) if at leastone of the two or more tables are empty, executing an apply outer joinfunction, wherein executing the apply outer join function creates a newtable of the two or more tables, and returning to step b); f) if none ofthe two or more tables are empty, identifying whether a vector R isempty, wherein the vector R is indicative of a merged version of the twoor more tables; g) if R is empty, executing the apply outer joinfunction, wherein executing the apply outer join function creates a newtable of the two or more tables and returning to step a); and h) if R isnot empty, generate a new table that includes information from the twoor more tables in a single table; and send, to a user device, the newtable and one or more commands directing the user device to display thenew table, wherein sending the one or more commands directing the userdevice to display the new table causes the user device to display thenew table.
 2. The computing platform of claim 1, wherein executing theend condition function comprises: extracting frequency data for columnsof the two or more tables with common values, wherein extracting thefrequency data comprises executing a columns with common values functionon a vector FDATA to identify a number of columns of the vector FDATAthat include the common values; and extracting a vector of columns withcommon values from the vector FDATA, wherein extracting the vector ofcolumns comprises executing the apply outer join function, wherein thevector of columns comprises the vector R.
 3. The computing platform ofclaim 2, wherein executing the columns with common values functioncomprises: for each column in a list of columns included in the two ormore tables: storing a first set of values for each column from a firsttable of the two or more tables, storing a second set of values for eachcolumn from a second table of the two or more tables, storing anintersection of the first set of values and the second set of values,identifying that the intersection includes at least one value, based onidentifying that the intersection includes the at least one value,storing a number of values comprising the intersection in the vectorFDATA; and outputting the vector FDATA.
 4. The computing platform ofclaim 2, wherein executing the apply outer join function comprises:applying an outerjoin function to join the at least two tables to createa sub-table; adding the sub-table to the vector R; setting the at leasttwo tables to be empty; and outputting the at least two tables and thevector R.
 5. The computing platform of claim 1, wherein executing thepartition tables function comprises: extracting frequency data forcolumns from the at least two tables with common values; generating avector FDATA using the frequency data; extracting, from the vectorFDATA, all columns for which values, common between the at least twotables, are greater than 1; generating a vector CCSPLIT using theextracted columns; identifying that CCSPLIT contains at least 1 column;based on identifying that CCSPLIT contains the at least 1 column:generate, for each row of the CCSPLIT columns, a row value vector thatincludes values of each of the rows of the CCSPLIT columns; identifyintersection vectors of the row value vectors and the extracted columns;store the intersection vectors as a vector CVAL, wherein the vector CVALcomprises an array of elements corresponding to the intersectionvectors; and for each element of the vector CVAL that contains a value:storing a corresponding column name from the extracted columns; applyinga split table function to generate new versions of the at least twotables; applying a statistical join function to generate a newsub-table, wherein the at the statistical join function merges the atleast two tables into the new sub-table; adding the new sub-table to thevector R; and removing, from each of the two or more tables, rowsbelonging to the new version of the corresponding table of the two ofmore tables; and outputting the two or more tables and the vector R. 6.The computing platform of claim 1, wherein executing the merge tablesfunction comprises: extracting frequency data for columns of the two ormore tables in a vector FDATA; inputting the vector FDATA into thecolumns with common values function; based on execution of the columnswith common values function on the vector FDATA, removing all columnsfor which values, common between the two or more tables are 1 and have afrequency of 1 in each of the two or more tables, resulting in a vectorCCMERGE; if the vector CCMERGE includes at least a single value:generating, for each column of the vector CCMERGE, generate a binarymask; for each binary mask: extracting a vector CMROWS comprising acollection of corresponding rows from the two or more tables with thegiven binary mask applied; for each element of the vector CMROWS thatincludes at least a single value: using a split table function to definenew versions of the two or more tables; executing an outerjoin functionto generate a new sub-table; adding the new sub-table to the vector R;and removing, from each of the two or more tables, rows belonging to thecorresponding new versions of the two or more tables; and outputting thetwo or more tables and the vector R.
 7. The computing platform of claim1, wherein the memory stores additional computer readable instructionsthat, when executed by the one or more processors, cause the computingplatform to: input the two or more tables into a statistical joinfunction, wherein inputting the two or more tables into the statisticaljoin function initiates the statistical join function.
 8. A methodcomprising: at a computing platform comprising at least one processor, acommunication interface, and memory: initiating execution of astatistical join function on two or more tables, wherein executing thestatistical join function comprises: a) if any of the two or more tablescomprise a single row, executing an end condition function and repeatingstep b), wherein executing the end condition function joins at least twoof the two or more tables comprising the single row and causes the atleast two of the two or more tables comprising the single row to nolonger comprise the single row; b) if none of the two or more tablescomprise a single row, executing a partition tables function; c) if thetwo or more tables are both empty, executing a statistical join functionto create a new table of the two or more tables, and returning to stepb); d) if the two or more tables are not both empty, executing a mergetables function; e) if at least one of the two or more tables are empty,executing an apply outer join function, wherein executing the applyouter join function creates a new table of the two or more tables, andreturning to step b); f) if none of the two or more tables are empty,identifying whether a vector R is empty, wherein the vector R isindicative of a merged version of the two or more tables; g) if R isempty, executing the apply outer join function, wherein executing theapply outer join function creates a new table of the two or more tablesand returning to step a); and h) if R is not empty, generate a new tablethat includes information from the two or more tables in a single table;and sending, to a user device, the new table and one or more commandsdirecting the user device to display the new table, wherein sending theone or more commands directing the user device to display the new tablecauses the user device to display the new table.
 9. The method of claim8, wherein executing the end condition function comprises: extractingfrequency data for columns of the two or more tables with common values,wherein extracting the frequency data comprises executing a columns withcommon values function on a vector FDATA to identify a number of columnsof the vector FDATA that include the common values; and extracting avector of columns with common values from the vector FDATA, whereinextracting the vector of columns comprises executing the apply outerjoin function, wherein the vector of columns comprises the vector R. 10.The method of claim 9, wherein executing the columns with common valuesfunction comprises: for each column in a list of columns included in thetwo or more tables: storing a first set of values for each column from afirst table of the two or more tables, storing a second set of valuesfor each column from a second table of the two or more tables, storingan intersection of the first set of values and the second set of values,identifying that the intersection includes at least one value, based onidentifying that the intersection includes the at least one value,storing a number of values comprising the intersection in the vectorFDATA; and outputting the vector FDATA.
 11. The method of claim 9,wherein executing the apply outer join function comprises: applying anouterjoin function to join the at least two tables to create asub-table; adding the sub-table to the vector R; setting the at leasttwo tables to be empty; and outputting the at least two tables and thevector R.
 12. The method of claim 8, wherein executing the partitiontables function comprises: extracting frequency data for columns fromthe at least two tables with common values; generating a vector FDATAusing the frequency data; extracting, from the vector FDATA, all columnsfor which values, common between the at least two tables, are greaterthan 1; generating a vector CCSPLIT using the extracted columns;identifying that CCSPLIT contains at least 1 column; based onidentifying that CCSPLIT contains the at least 1 column: generate, foreach row of the CCSPLIT columns, a row value vector that includes valuesof each of the rows of the CCSPLIT columns; identify intersectionvectors of the row value vectors and the extracted columns; store theintersection vectors as a vector CVAL, wherein the vector CVAL comprisesan array of elements corresponding to the intersection vectors; and foreach element of the vector CVAL that contains a value: storing acorresponding column name from the extracted columns; applying a splittable function to generate new versions of the at least two tables;applying a statistical join function to generate a new sub-table,wherein the at the statistical join function merges the at least twotables into the new sub-table; adding the new sub-table to the vector R;and removing, from each of the two or more tables, rows belonging to thenew version of the corresponding table of the two of more tables; andoutputting the two or more tables and the vector R.
 13. The method ofclaim 8, wherein executing the merge tables function comprises:extracting frequency data for columns of the two or more tables in avector FDATA; inputting the vector FDATA into the columns with commonvalues function; based on execution of the columns with common valuesfunction on the vector FDATA, removing all columns for which values,common between the two or more tables are 1 and have a frequency of 1 ineach of the two or more tables, resulting in a vector CCMERGE; if thevector CCMERGE includes at least a single value: generating, for eachcolumn of the vector CCMERGE, generate a binary mask; for each binarymask: extracting a vector CMROWS comprising a collection ofcorresponding rows from the two or more tables with the given binarymask applied; for each element of the vector CMROWS that includes atleast a single value: using a split table function to define newversions of the two or more tables; executing an outerjoin function togenerate a new sub-table; adding the new sub-table to the vector R; andremoving, from each of the two or more tables, rows belonging to thecorresponding new versions of the two or more tables; and outputting thetwo or more tables and the vector R.
 14. The method of claim 8, furthercomprising: inputting the two or more tables into a statistical joinfunction, wherein inputting the two or more tables into the statisticaljoin function initiates the statistical join function.
 15. One or morenon-transitory computer-readable media storing instructions that, whenexecuted by a computing platform comprising at least one processor, acommunication interface, and memory, cause the computing platform to:initiate execution of a statistical join function on two or more tables,wherein executing the statistical join function comprises: a) if any ofthe two or more tables comprise a single row, executing an end conditionfunction and repeating step b), wherein executing the end conditionfunction joins at least two of the two or more tables comprising thesingle row and causes the at least two of the two or more tablescomprising the single row to no longer comprise the single row; b) ifnone of the two or more tables comprise a single row, executing apartition tables function; c) if the two or more tables are both empty,executing a statistical join function to create a new table of the twoor more tables, and returning to step b); d) if the two or more tablesare not both empty, executing a merge tables function; e) if at leastone of the two or more tables are empty, executing an apply outer joinfunction, wherein executing the apply outer join function creates a newtable of the two or more tables, and returning to step b); f) if none ofthe two or more tables are empty, identifying whether a vector R isempty, wherein the vector R is indicative of a merged version of the twoor more tables; g) if R is empty, executing the apply outer joinfunction, wherein executing the apply outer join function creates a newtable of the two or more tables and returning to step a); and h) if R isnot empty, generate a new table that includes information from the twoor more tables in a single table; and send, to a user device, the newtable and one or more commands directing the user device to display thenew table, wherein sending the one or more commands directing the userdevice to display the new table causes the user device to display thenew table.
 16. The one or more non-transitory computer-readable media ofclaim 15, wherein executing the end condition function comprises:extracting frequency data for columns of the two or more tables withcommon values, wherein extracting the frequency data comprises executinga columns with common values function on a vector FDATA to identify anumber of columns of the vector FDATA that include the common values;and extracting a vector of columns with common values from the vectorFDATA, wherein extracting the vector of columns comprises executing theapply outer join function, wherein the vector of columns comprises thevector R.
 17. The one or more non-transitory computer-readable media ofclaim 16, wherein executing the columns with common values functioncomprises: for each column in a list of columns included in the two ormore tables: storing a first set of values for each column from a firsttable of the two or more tables, storing a second set of values for eachcolumn from a second table of the two or more tables, storing anintersection of the first set of values and the second set of values,identifying that the intersection includes at least one value, based onidentifying that the intersection includes the at least one value,storing a number of values comprising the intersection in the vectorFDATA; and outputting the vector FDATA.
 18. The one or morenon-transitory computer-readable media of claim 16, wherein executingthe apply outer join function comprises: applying an outerjoin functionto join the at least two tables to create a sub-table; adding thesub-table to the vector R; setting the at least two tables to be empty;and outputting the at least two tables and the vector R.
 19. The one ormore non-transitory computer-readable media of claim 15, whereinexecuting the merge tables function comprises: extracting frequency datafor columns of the two or more tables in a vector FDATA; inputting thevector FDATA into the columns with common values function; based onexecution of the columns with common values function on the vectorFDATA, removing all columns for which values, common between the two ormore tables are 1 and have a frequency of 1 in each of the two or moretables, resulting in a vector CCMERGE; if the vector CCMERGE includes atleast a single value: generating, for each column of the vector CCMERGE,generate a binary mask; for each binary mask: extracting a vector CMROWScomprising a collection of corresponding rows from the two or moretables with the given binary mask applied; for each element of thevector CMROWS that includes at least a single value: using a split tablefunction to define new versions of the two or more tables; executing anouterjoin function to generate a new sub-table; adding the new sub-tableto the vector R; and removing, from each of the two or more tables, rowsbelonging to the corresponding new versions of the two or more tables;and outputting the two or more tables and the vector R.
 20. The one ormore non-transitory computer-readable media of claim 15, wherein thememory stores additional computer readable instructions that, whenexecuted by the one or more processors, cause the computing platform to:input the two or more tables into a statistical join function, whereininputting the two or more tables into the statistical join functioninitiates the statistical join function.